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Developing a human performance railway operational index to enhance safety of railway operations

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Title: Developing a human performance railway operational index to enhance safety of railway operations
Authors: Kyriakidis, Miltos
Item Type: Thesis or dissertation
Abstract: The railway system is a complex network that involves continuous interaction of human operators with technology, procedures and regulations to ensure safe and efficient operations. From an architectural perspective, the complexity of the interactions presents a risk of failure with the consequence that safety incidents and accidents may occur. The common approach to the development of measures for mitigating such occurrences is the retrospective analysis of accidents and incidents; in order firstly to identify, classify and acknowledge the contributing factors and secondly, to suggest mitigation strategies. Research undertaken globally using retrospective analysis indicates that a large number of railway accidents and incidents are associated with human errors due to degraded human performance. In particular, it has been shown that train operators (drivers, signallers and controllers) account for the majority of accidents and incidents. For example, between 1990 and 2009, at least 75% of fatal railway accidents in Europe were due to excessive speed, signal passed at danger or signalling/dispatching errors. There has been a significant research effort to examine, identify and understand the factors that affect human performance in railway operations, so as to prevent conditions related to degrade performance and to reduce the probability of human errors. However current methods, developed on the principles of Human Reliability Analysis (HRA), are based on research from other domains, including nuclear, oil and gas, and aviation. Hence, they are not suited to the rail industry and can be difficult to apply reliably to railway specific operations. Moreover, in the case of the factors that affect human performance, current methodologies have either adopted lists of factors from other domains or slightly modified existing lists, and then applied them to the railway industry. In addition, even in the cases where the lists of factors have been modified, such alterations have been designed on the basis of regional accident and incident analysis. Although the number of factors that influence performance can be claimed to be limited, e.g. fatigue, training, organisational culture and system design, the analysis of only regional occurrences does not provide analysts with a worldwide perspective of the significance of factors on human performance. Therefore, this thesis addresses the current limitations and proposes a new framework to identify the factors that affect the performance of railway operators, and assess human performance. In particular, this thesis developed for the first time a novel and comprehensive taxonomy for railway operations, referred to as the Railway-Performance Shaping Factors (R-PSFs) taxonomy. The taxonomy is derived from a variety of sources including: extensive literature review, operators’ hierarchical task analysis, and the analysis of global accidents and incidents. Subject matter experts validated the taxonomy. Results identified 43 contributing factors, whilst further statistical analysis indicates that 12 out of 43 factors are responsible for more than 90% of total occurrences regardless of the type of network, responsibility and severity of consequences. Unlike current taxonomies, the framework developed accounts for both the influence of each individual factor and the dynamic interactive influence of the factors due to their mutual dependencies. It is recommended that the R-PSFs taxonomy be used by railway stakeholders to enhance the Safety Management Systems of their organisation. In addition the taxonomy can be used as part of the training program of the organisations in order to inform and engage the railway personnel with respect to the factors that primarily affect their performance. Finally, the taxonomy is recommended for use by the investigator stakeholders to obtain information about the human aspect that may have led to railway occurrences. This thesis also developed, tested and validated a framework, referred to as the Human Performance (HuPeROI) to enhance safety in railway operations. Based on the 12 largest contributing factors, the HuPeROI is a novel scheme to assess human performance, as function of the various R-PSFs. The HuPeROI for the first time introduces an approach to quantify the impact of each of the factors that affect human performance accounting for all the dependencies amongst those factors. HuPeROI has been developed by integrating the generic concept of two techniques, the Analytic Network Process and the Success Likelihood Index Methodology (SLIM). The former is one of the best known and widely used multi-criteria decision making techniques and was used to evaluate the influence of each R-PSF on operators’ performance. SLIM was applied to rate the importance of each of the R-PSFs for different operational actions and finally to estimate the reliability index for these actions. The HuPeROI framework was demonstrated in a case study in three different types of railway operations: regional, high-speed and underground, and helps to define the influence of each individual factor on human performance as well as to indicate the relative likelihoods of different human errors. Finally, both the R-PSFs taxonomy and HuPeROI can be transferred and used with minor modifications not only in other railway procedures, e.g. maintenance, but also domains, e.g. aviation, maritime and oil.
Content Version: Open Access
Issue Date: Oct-2013
Date Awarded: Jan-2014
URI: http://hdl.handle.net/10044/1/21760
DOI: https://doi.org/10.25560/21760
Supervisor: Majumdar, Arnab
Ochieng, Washington
Sponsor/Funder: Lloyd's Register Foundation
Department: Civil and Environmental Engineering
Publisher: Imperial College London
Qualification Level: Doctoral
Qualification Name: Doctor of Philosophy (PhD)
Appears in Collections:Civil and Environmental Engineering PhD theses

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